Can Recursion Make LLMs Smarter and More Efficient?

Author(s): Arthur Lagacherie

Originally published on Towards AI.

Recursion could reshape how LLMs scale.

A major problem with current LLM architectures is the difficulty of adapting their computational power to match the performance requirements of specific tasks (low performance requirements should use low computing power, and vice versa).

This article discusses two recent papers focusing on recursion in LLMs: one aims to enhance efficiency through parameter reuse while the other uses a new approach that allows for unrestricted recursion depths to enhance performance. It analyzes the benefits and drawbacks of both architectures, emphasizing that while one model shows superior benchmark performance, the other offers greater flexibility and scaling potential. The conclusion suggests a hybrid model combining both methodologies for optimal performance and efficiency.

Read the full blog for free on Medium.

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Published via Towards AI